Machine Learning-Based Prediction modelling of EDM application on Ti6Al4V alloy using 3D-Printed SS316L Electrodes
DOI:
https://doi.org/10.68104/ijasit.v1.i3.28Keywords:
EDM, 3D Fabricated SS316L electrode, Ti6Al4V alloy, Machine learningAbstract
This study explores the application of 3D-printed SS316L electrodes in the Electrical Discharge Machining (EDM) of Ti6Al4V alloy and employs machine learning (ML) models to predict and optimize key performance metrics such as Material Removal Rate (MRR), Tool Wear Rate (TWR), surface roughness, and hardness. EDM experiments were conducted using a Box–Behnken and L27 orthogonal array design, and data-driven models were trained using four regression algorithms. Initial experiments with 27 data points showed limited model accuracy (R² = 56.2% for MRR using Random Forest). To enhance prediction capability, the dataset was expanded to 200 samples using polynomial interpolation. After expansion, Random Forest achieved 95.6% R² in predicting MRR and TWR, while XGBoost reached 98.6% R² for surface roughness and hardness. These findings demonstrate that integrating 3D-printed electrodes with AI-driven modellingand improving the data set significantly improves the predictive control.
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